research-article

QoS-based service optimization using differential evolution

Authors:

Florin-Claudiu Pop,

Marcel Cremene,

Andrea Tettamanzi,

Mihai Suciu, and

Mircea VaidaAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

Pages 1891 - 1898

https://doi.org/10.1145/2001576.2001830

Published: 12 July 2011 Publication History

Abstract

The aim of our research is to find an efficient solution to the services QoS optimization problem. This NP-hard problem is well known in the service-oriented computing field: given a business workflow that includes a set of abstract services and a set of concrete service implementations for each abstract service, the goal is to find the optimal combination of concrete services. The majority of recent proposals indicate the Genetic Algorithms (GA) as the best approach for complex workflows. But this problem usually needs to be solved at runtime, a task for which GA may be too slow. We propose a new approach, based on Differential Evolution (DE), that converges faster and it is more scalable and robust than the existing solutions based on Genetic Algorithms.

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Cited By

Jing WXue YWang J(2023)An Enhanced Energy-Efficient Web Service Composition Algorithm Based on the Firefly AlgorithmJournal of Database Management10.4018/JDM.32174034:1(1-19)Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.4018/JDM.321740
Zhu MYu FYan XLi JWang Y(2021)Scaling up Mobile Service Selection in Edge Computing Environment with Cuckoo Optimization Algorithm2021 IEEE International Conference on Services Computing (SCC)10.1109/SCC53864.2021.00056(394-400)Online publication date: Sep-2021
https://doi.org/10.1109/SCC53864.2021.00056
Gorski TWozniak A(2021)Optimization of Business Process Execution in Services Architecture: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2021.31026689(111833-111852)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3102668
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Index Terms

QoS-based service optimization using differential evolution
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation

July 2011

2140 pages

ISBN:9781450305570

DOI:10.1145/2001576

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2011

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GECCO '11

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Jing WXue YWang J(2023)An Enhanced Energy-Efficient Web Service Composition Algorithm Based on the Firefly AlgorithmJournal of Database Management10.4018/JDM.32174034:1(1-19)Online publication date: 20-Apr-2023
https://dl.acm.org/doi/10.4018/JDM.321740
Zhu MYu FYan XLi JWang Y(2021)Scaling up Mobile Service Selection in Edge Computing Environment with Cuckoo Optimization Algorithm2021 IEEE International Conference on Services Computing (SCC)10.1109/SCC53864.2021.00056(394-400)Online publication date: Sep-2021
https://doi.org/10.1109/SCC53864.2021.00056
Gorski TWozniak A(2021)Optimization of Business Process Execution in Services Architecture: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2021.31026689(111833-111852)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3102668
Masdari MNozad Bonab MOzdemir S(2021)QoS-driven metaheuristic service composition schemes: a comprehensive overviewArtificial Intelligence Review10.1007/s10462-020-09940-454:5(3749-3816)Online publication date: 1-Jun-2021
https://dl.acm.org/doi/10.1007/s10462-020-09940-4
da Silva AMa HMei YZhang M(2020)A Survey of Evolutionary Computation for Web Service Composition: A Technical PerspectiveIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2020.29692134:4(538-554)Online publication date: Aug-2020
https://doi.org/10.1109/TETCI.2020.2969213
Purohit LKumar S(2020)A study on evolutionary computing based web service selection techniquesArtificial Intelligence Review10.1007/s10462-020-09872-zOnline publication date: 7-Aug-2020
https://doi.org/10.1007/s10462-020-09872-z
Zhao XLi RZuo X(2019)Advances on QoS‐aware web service selection and composition with nature‐inspired computingCAAI Transactions on Intelligence Technology10.1049/trit.2019.00184:3(159-174)Online publication date: 6-Sep-2019
https://doi.org/10.1049/trit.2019.0018
Guo XChen SZhang YLi W(2017)Service Composition Optimization Method Based on Parallel Particle Swarm Algorithm on SparkSecurity and Communication Networks10.1155/2017/90976162017Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1155/2017/9097616
Zhang WYang YZhang SYu DChen Y(2017)A new three-dimensional manufacturing service composition method under various structures using improved Flower Pollination AlgorithmEnterprise Information Systems10.1080/17517575.2017.141089512:5(620-637)Online publication date: 30-Nov-2017
https://doi.org/10.1080/17517575.2017.1410895
Mesquita CCavalca UPereira ACarrano EPrazeres CSampaio PSantanchè ASantos CGoularte R(2013)A traffic shaping optimization methodology for web systemsProceedings of the 19th Brazilian symposium on Multimedia and the web10.1145/2526188.2526190(209-216)Online publication date: 5-Nov-2013
https://dl.acm.org/doi/10.1145/2526188.2526190
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